The next frontier of AI training data is inside companies

The public web taught AI models to read and write. It did not teach them how a team ships a release, closes a quarter or resolves an angry customer over three weeks. That knowledge lives inside companies, in the chat, email, tickets and code they produce every day, and it is where the next useful supply of training data will come from.

Web text is broad. It covers almost every topic, in almost every register, and it is the reason today's models can explain a concept or draft a paragraph. But most of it is finished output: the published article, the answered forum question, the polished documentation page.

What it rarely shows is the work in between. The draft that got rejected. The Slack thread where two engineers disagreed about a schema. The support ticket that bounced between three teams before someone found the root cause. Those intermediate steps are exactly what an agent needs to see if it is going to take actions, not just produce text.

Every company, even a small one, generates a detailed record of how it runs. It is spread across the tools the team used:

• Chat in Slack or Teams, where questions get asked and decisions get made quickly
• Email, including threads with customers, vendors, auditors and partners
• Docs and wikis, where plans are written, revised and sometimes abandoned
• Tickets and projects in Jira, Linear or Asana, with status changes and owners over time
• Code and pull requests, with review comments and the reasons behind changes
• CRM and customer support, showing how deals move and how problems get solved
• Finance workflows, from purchase approvals to the month-end close

Any one of these is interesting. Together they are something the public web cannot offer: the same piece of work, traced across several systems, over months or years. A feature request shows up in a support ticket, gets discussed in chat, becomes a Jira issue, turns into a pull request and is announced in an email to customers. That chain is what we mean when we say connected data beats big data.

If company data is this useful, why has so little of it reached AI training? A few reasons, and none of them are about the data itself.

First, privacy. Company records are full of names, email addresses, phone numbers, customer details and the occasional password pasted into a chat. Nobody responsible would hand that over raw, and they should not.

Second, there has been no clear process. A founder or CFO who wondered whether their archive had value had no obvious person to call, no standard contract and no way to know what they were agreeing to.

Third, the companies with the richest records are usually busy running a business, or busy closing one. They are not shopping their Slack history around, and they are not going to fill in a long form on a marketplace on the off chance someone is interested.

The pieces needed to license company data responsibly are not exotic. They just have to be done carefully and in the right order.

Anonymization comes first. Every person, company, address, email, phone number and account is replaced with a consistent token, so PERSON_014 is the same person in Slack, email and Jira. Credentials, API keys, card numbers and government IDs are removed outright. Faces, logos and signatures in images and PDFs are blacked out. HR, payroll, medical and legally privileged folders are excluded by default. The full approach is on our trust page.

Scope comes next. The company decides what is in and what is out, including specific clients, channels or folders, and those exclusions are annexed to the contract before any data moves. The licence itself is narrow: training and evaluation only, with buyers prohibited from marketing to, profiling, contacting or re-identifying anyone in the data.

Then deletion. The raw archive is deleted within 30 days of sign-off, as a contract obligation, with a deletion certificate. No third party ever touches the raw data.

For companies, it turns a byproduct of doing business into a source of revenue. That applies to operating companies with years of history and to companies winding down, where the data would otherwise disappear when the subscriptions lapse. Typical licences range from $20K to $400K+, depending on how deep and connected the records are, how many years they span, how many people produced them and how many systems they cover. No one can give a firm number before the data is counted.

The seller's time commitment is small. A 15-minute intro call, a read-only evaluation that takes under an hour of the seller's time, a review of an anonymized sample, and payment to the company entity within two to four weeks. About an hour in total.

For AI labs, it opens up a source that is hard to fake. Synthetic data can approximate a company, but it struggles to reproduce the messy, inconsistent, long-running texture of real work. Private company records also have a practical advantage for evaluation: they have never been on the public web, so they are not sitting in anyone's pretraining set.

The shift is straightforward. Models learned language from what people published. Agents will learn work from what people did. The record of what people did lives in company tools, and the companies that hold it have a reasonable, well-defined way to put it to use without exposing the people in it.

If you run or advise a company with a few years of history across several tools, it is worth finding out what that record is worth. Book a 15-minute call and we will walk you through it.